Smart manufacturing requires flexible production organization and management to handle the dynamic customer requirements rapidly and efficiently. In the context of smart manufacturing, work-in-progress (WIP), machines, and other physical resources in smart shop floors are endowed with intelligence, such as self-perception and self-decision-making. In this situation, the manufacturing task orchestration in such smart shop floors becomes autonomous, which is different from the traditional one that is centrally set and managed. The manufacturing tasks are accomplished with the help of autonomous communication between the WIP and the machines. This paper firstly clarifies the logic of autonomous manufacturing, in which the core idea is the autonomous communication and collaboration between the WIP and the machines during production. Furthermore, the autonomous manufacturing task orchestration (AMTO) problem is described. An improved hidden Markov model (HMM) is proposed to formulate the problem and generate an optimal AMTO solution for a certain process flow. A demonstrative case is implemented to verify the feasibility of the proposed model and method. The results show that HMM can give suggestions on AMTO and dynamically adjust the situation based on the real-time manufacturing data.

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

Increasing individualization demands in products call for high flexibility in the manufacturing systems to adapt changes. This paper proposes a novel digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. The digital twin comprises two parts, the semi-physical simulation that maps data of the system and provides input data to the second part, which is optimization. The results of the optimization part are fed back to the semi-physical simulation for verification. Open-architecture machine tool (OAMT) is defined and developed as a new class of machine tools comprising a fixed standard platform and various individualized modules that can be added and rapidly swapped. Engineers can flexibly reconfigure the manufacturing system for catering to process planning by integrating personalized modules into its OAMTs. Key enabling techniques, including how to twin cyber and physical system and how to quickly bi-level program the production capacity and functionality of manufacturing systems to adapt rapid changes of products, are detailed. A physical implementation is conducted to verify the effectiveness of the proposed approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it.

To create a service system for equipment lifecycle management by using radio frequency identification devices (RFID) technology under the existing industrial Internet framework, is a solution for the confusion of label identifying, loss of data, low-timeliness of data, the difficulties of data query and the low-traceability of products that occurred in the process of equipment lifecycle management. It can be used to collect the real-time data automatically and mark the unique identification in the process for the critical equipment and parts. By docking the service system for equipment lifecycle management with other enterprise management systems like MES, WMS and EAM, the data correlation among different stages of equipment lifecycle will be realized and an accurate real-time data sharing platform will be built efficiently. In addition, this system offers the realtime dynamic information and decision support for the equipment lifecycle management.